Method and system for providing formatted data to image processing means in accordance with a standard format

ABSTRACT

A method and system for providing, in accordance with a standard format, formatted information to an image-processor, especially software and/or components. The formatted information is related to defects of a chain of appliances including an image-capture appliance and/or an image-restitution appliance. The image-processor uses the formatted data to modify the quality of at least one image derived from or addressed to the chain of appliances. The formatted information includes data characterizing defects of the image-capture appliance, especially distortion characteristics, and/or data characterizing defects of the image-restitution appliance, especially distortion characteristics. The method fills in at least one field of the standard format with the formatted information. This field is designated by a field name and contains at least one field value.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to a method and a system for providingformatted information in a standard format to image-processing means.

SUMMARY OF THE INVENTION

The invention relates to a method for providing formatted information ina standard format to image-processing means, especially software and/orcomponents. The formatted information is related to the defects of anappliance chain. The appliance chain includes in particular at least oneimage-capture appliance and/or one image-restitution appliance. Theimage-processing means use the formatted information to modify thequality of at least one image derived from or addressed to the saidappliance chain. The formatted information includes data characterizingthe defects of the image-capture appliance, especially the distortioncharacteristics, and/or data characterizing the defects of theimage-restitution appliance, especially the distortion characteristics.

The method includes the stage of filling in at least one field of thesaid standard format with the formatted information. The field isdesignated by a field name. The field contains at least one field value.

Preferably, according to the invention, the method is such that thefield is related to the sharpness defects of the image-capture applianceand/or of the image-restitution appliance. The method is such that thefield contains at least one value related to the sharpness defects ofthe image-capture appliance and/or of the image-restitution appliance.

Preferably, according to the invention, the method is such that thefield is related to the colorimetry defects of the image-captureappliance and/or of the image-restitution appliance. The method is suchthat the field contains at least one value related to the colorimetrydefects of the image-capture appliance and/or of the image-restitutionappliance.

Preferably, according to the invention, the method is such that thefield is related to the geometric distortion defects and/or to thegeometric chromatic aberration defects of the image-capture applianceand/or of the image-restitution appliance. The method is such that thefield contains at least one value related to the geometric distortiondefects and/or to the geometric chromatic aberration defects of theimage-capture appliance and/or of the image-restitution appliance.

Preferably, according to the invention, the method is such that thefield is related to the geometric vignetting defects and/or to thecontrast defects of the image-capture appliance and/or of theimage-restitution appliance. The method is such that the field containsat least one value related to the geometric vignetting defects and/or tothe contrast defects of the image-capture appliance and/or of theimage-restitution appliance.

Preferably, according to the invention, the method is such that thefield contains at least one value related to the deviations.

Preferably, according to the invention, the formatted information iscomposed at least partly of parameters of a parameterizabletransformation model representative of the sharpness defects of theimage-capture appliance and/or of the restitution appliance. The methodis such that the value or values contained in the field related to thesharpness defects are composed at least partly of parameters of theparameterizable transformation model. It results from the combination oftechnical features that the image-processing means can use theparameters of the parameterizable transformation model to calculate thecorrected shape or the corrected restitution shape of an image point.

Preferably, according to the invention, the formatted information iscomposed at least partly of parameters of a parameterizabletransformation model representative of the colorimetry defects of theimage-capture appliance and/or of the restitution appliance. The methodis such that the value or values contained in the field related to thecolorimetry defects are composed at least partly of parameters of theparameterizable transformation model. It results from the combination oftechnical features that the image-processing means can use theparameters of the parameterizable transformation model to calculate thecorrected color or the corrected restitution color of an image point.

Preferably, according to the invention, the formatted information iscomposed at least partly of parameters of a parameterizabletransformation model representative of the geometric distortion defectsand/or of the geometric chromatic aberration defects of theimage-capture appliance and/or of the restitution appliance. The methodis such that the value or values contained in the field related to thegeometric distortion defects and/or to the geometric chromaticaberration defects are composed at least partly of parameters of theparameterizable transformation model. It results from the combination oftechnical features that the image-processing means can use theparameters of the parameterizable transformation model to calculate thecorrected position or the corrected restitution position of an imagepoint.

Preferably, according to the invention, the formatted information iscomposed at least partly of parameters of a parameterizabletransformation model representative of the geometric vignetting defectsand/or of the contrast defects of the image-capture appliance and/or ofthe restitution appliance. The method is such that the value or valuescontained in the field related to the geometric vignetting defectsand/or to the contrast defects are composed at least partly ofparameters of the parameterizable transformation model. It results fromthe combination of technical features that the image-processing meanscan use the parameters of the parameterizable transformation model tocalculate the corrected intensity or the corrected restitution intensityof an image point.

Association of Formatted Information with the Image

Preferably, according to the invention, in order to provide formattedinformation in a standard format to image-processing means, the methodadditionally includes the stage of associating the formatted informationwith the image.

Preferably, according to the invention, the image is transmitted in theform of a file. The file additionally contains the formattedinformation.

Variable Focal Length

Preferably, according to the invention, the image-capture applianceand/or the image-restitution appliance includes at least one variablecharacteristic depending on the image, especially the focal length. Atleast one of the defects of the image-capture appliance and/or of theimage-restitution appliance, especially the geometric distortion defect,depends on the variable characteristic. The method is such that at leastone of the fields contains at least one value that is a function of thevariable characteristic depending on the image. It results from thecombination of technical features that the image-processing means canprocess the image as a function of the variable characteristics.

Measured Formatted Information

Preferably, according to the invention, the formatted information ismeasured formatted information, at least in part. Thus, in the case ofthis alternative embodiment, the defects are small.

Preferably, according to the invention, the formatted information isextended formatted information, at least in part. Thus, in the case ofthis alternative embodiment, the formatted information occupies littlememory. Thus also, the image-processing calculations are faster.

The image can be composed of color planes. Preferably in the case ofthis alternative embodiment according to the invention, the formattedinformation is at least partly related to the color planes. It resultsfrom the combination of technical features that the processing of theimage can be separated into processing operations related to each colorplane. It results from the combination of technical features that, bydecomposing the image into color planes before processing, it ispossible to arrive at positive pixel values in the color planes.

System

The invention relates to a system for providing formatted information ina standard format to image-processing means, especially software and/orcomponents. The formatted information is related to the defects of anappliance chain. The appliance chain includes in particular at least oneimage-capture appliance and/or one image restitution-appliance. Theimage-processing means use the formatted information to modify thequality of at least one image derived from or addressed to the appliancechain. The formatted information includes data characterizing thedefects of the image-capture appliance, especially the distortioncharacteristics, and/or data characterizing the defects of theimage-restitution appliance, especially the distortion characteristics.

The system includes data-processing means for filling in at least onefield of the standard format with the formatted information. The fieldis designated by a field name. The field contains at least one fieldvalue.

Preferably, according to the invention, the system is such that thefield is related to the sharpness defects of the image-capture applianceand/or of the image-restitution appliance. The system is such that thefield contains at least one value related to the sharpness defects ofthe image-capture appliance and/or of the image-restitution appliance.

Preferably, according to the invention, the system is such that thefield is related to the colorimetry defects of the image-captureappliance and/or of the image-restitution appliance. The system is suchthat the field contains at least one value related to the colorimetrydefects of the image-capture appliance and/or of the image-restitutionappliance.

Preferably, according to the invention, the system is such that thefield is related to the geometric distortion defects and/or to thegeometric chromatic aberration defects of the image-capture applianceand/or of the image-restitution appliance. The system is such that thefield contains at least one value related to the geometric distortiondefects and/or to the geometric chromatic aberration defects of theimage-capture appliance and/or of the image-restitution appliance.

Preferably, according to the invention, the system is such that thefield is related to the geometric vignetting defects and/or to thecontrast defects of the image-capture appliance and/or of theimage-restitution appliance. The system is such that the field containsat least one value related to the geometric vignetting defects and/or tothe contrast defects of the image-capture appliance and/or of theimage-restitution appliance.

Preferably, according to the invention, the system is such that thefield contains at least one value related to the deviations.

Preferably, according to the invention, the formatted information iscomposed at least partly of parameters of a parameterizabletransformation model representative of the sharpness defects of theimage-capture appliance and/or of the restitution appliance. The systemis such that the value or values contained in the field related to thesharpness defects are composed at least partly of parameters of theparameterizable transformation model.

Preferably, according to the invention, the formatted information iscomposed at least partly of parameters of a parameterizabletransformation model representative of the colorimetry defects of theimage-capture appliance and/or of the restitution appliance. The systemis such that the value or values contained in the field related to thecolorimetry defects are composed at least partly of parameters of theparameterizable transformation model.

Preferably, according to the invention, the formatted information iscomposed at least partly of parameters of a parameterizabletransformation model representative of the geometric distortion defectsand/or of the geometric chromatic aberration defects of theimage-capture appliance and/or of the restitution appliance. The systemis such that the value or values contained in the field related to thegeometric distortion defects and/or to the geometric chromaticaberration defects are composed at least partly of parameters of theparameterizable transformation model.

Preferably, according to the invention, the formatted information iscomposed at least partly of parameters of a parameterizabletransformation model representative of the geometric vignetting defectsand/or of the contrast defects of the image-capture appliance and/or ofthe restitution appliance. The system is such that the value or valuescontained in the field related to the geometric vignetting defectsand/or to the contrast defects are composed at least partly ofparameters of the parameterizable transformation model.

Association of Formatted Information with the Image

Preferably, according to the invention, in order to provide formattedinformation in a standard format to image-processing means, the systemadditionally includes data-processing means for associating theformatted information with the image.

Preferably, according to the invention, the system includes transmissionmeans for transmitting the image in the form of a file. The fileadditionally contains the formatted information.

Variable Focal Length

The image-capture appliance and/or the image-restitution appliance caninclude at least one variable characteristic depending on the image,especially the focal length. At least one of the defects of theimage-capture appliance and/or of the image-restitution appliance,especially the geometric distortion defect, depends on the variablecharacteristic. Preferably in the case of this alternative embodimentaccording to the invention, the system is such that at least one of thefields contains at least one value that is a function of the variablecharacteristic depending on the image.

Alternative Versions of Formatted Information

Preferably, according to the invention, the formatted information ismeasured formatted information, at least in part.

Preferably, according to the invention, the formatted information isextended formatted information, at least in part.

The image can be composed of color planes. Preferably in the case ofthis alternative embodiment according to the invention, the formattedinformation is at least partly related to the color planes.

BRIEF DESCRIPTION OF THE DRAWINGS

Other characteristics and advantages of the invention will becomeapparent upon reading of the description of alternative embodiments ofthe invention, provided by way of indicative and non-limitativeexamples, and of the figures, wherein respectively:

FIG. 1 illustrates a schematic view of image capture,

FIG. 2 illustrates a schematic view of image restitution,

FIG. 3 illustrates a schematic view of the pixels of an image,

FIGS. 4 a and 4 b illustrate two schematic views of a reference scene,

FIG. 5 illustrates the organizational diagram of the method with whichthe difference between the mathematical image and the corrected imagecan be calculated,

FIG. 6 illustrates the organizational diagram of the method with whichthe best restitution transformation for an image-restitution means canbe obtained,

FIG. 7 illustrates a schematic view of the elements composing the systemaccording to the invention,

FIG. 8 illustrates a schematic view of fields of formatted information,

FIG. 9 a illustrates a schematic front view of a mathematical point,

FIG. 9 b illustrates a schematic front view of a real point of an image,

FIG. 9 c illustrates a schematic side view of a mathematical point,

FIG. 9 d illustrates a schematic profile view of a real point of animage,

FIG. 10 illustrates a schematic view of an array of characteristicpoints,

FIG. 11 illustrates the organizational diagram of the method with whichthe formatted information can be obtained,

FIG. 12 illustrates the organizational diagram of the method with whichthe best transformation for an image-capture appliance can be obtained,

FIG. 13 illustrates the organizational diagram of the method with whichthe quality of an image derived from or addressed to a chain ofappliances can be modified,

FIG. 14 illustrates an example of a file containing formattedinformation,

FIG. 15 illustrates an example of formatted information,

FIG. 16 illustrates a representation of parameters of parameterizablemodels,

FIG. 17 illustrates an organizational diagram of the method with whichthe best transformation for an image-restitution appliance can beobtained.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 illustrates a scene 3 containing an object 107, a sensor 101 andsensor surface 110, an optical center 111, an observation point 105 on asensor surface 110, an observation direction 106 passing throughobservation point 105, optical center 111, scene 3, and a surface 10geometrically associated with sensor surface 110.

FIG. 2 illustrates an image 103, an image-restitution means 19 and arestituted image 191 obtained on the restitution medium 190.

FIG. 3 illustrates a scene 3, an image-capture appliance 1 and an image103 composed of pixels 104.

FIGS. 4 a and 4 b illustrate two alternative versions of a referencescene 9.

FIG. 5 illustrates an organizational diagram employing a scene 3, amathematical projection 8 giving a mathematical image 70 of scene 3, areal projection 72 giving an image 103 of scene 3 for thecharacteristics 74 used, a parameterizable transformation model 12giving a corrected image 71 of image 103, the corrected image 71exhibiting a difference 73 compared with mathematical image 70.

FIG. 6 illustrates an organizational diagram employing an image 103, areal restitution projection 90 giving a restituted image 191 of image103 for the restitution characteristics 95 used, a parameterizablerestitution transformation model 97 giving a corrected restitution image94 of image 103, a mathematical restitution projection 96 giving amathematical restitution image 92 of corrected restitution image 94 andexhibiting a restitution difference 93 compared with restituted image191.

FIG. 7 illustrates a system comprising an image-capture appliance 1composed of an optical system 100, of a sensor 101 and of an electronicunit 102. FIG. 7 also illustrates a memory zone 16 containing an image103, a database 22 containing formatted information 15, and means 18 fortransmission of completed image 120 composed of image 103 and formattedinformation 15 to calculating means 17 containing image-processingsoftware 4.

FIG. 8 illustrates formatted information 15 composed of fields 91.

FIGS. 9 a to 9 d illustrate a mathematical image 70, an image 103, themathematical position 40 of a point, and the mathematical shape 41 of apoint, compared with the real position 50 and the real shape 51 of thecorresponding point of the image.

FIG. 10 illustrates an array 80 of characteristic points.

FIG. 11 illustrates an organizational diagram employing an image 103,the characteristics 74 used, and a database 22 of characteristics. Theformatted information 15 is obtained from the characteristics 74 usedand stored in database 22. The completed image 120 is obtained fromimage 103 and formatted information 15.

FIG. 12 illustrates an organizational diagram employing a referencescene 9, a mathematical projection 8 giving a synthetic image class 7 ofreference scene 9, and a real projection 72 giving a reference image 11of reference scene 9 for the characteristics 74 used. Thisorganizational diagram also employs a parameterizable transformationmodel 12 giving a transformed image 13 of reference image 11.Transformed image 13 exhibits a deviation 14 compared with syntheticimage class 7.

FIG. 17 illustrates an organizational diagram employing a restitutionreference 209, a real restitution projection 90 giving a restitutedreference 211 of the said restitution reference 209 for the restitutioncharacteristics 95 used, a parameterizable restitution transformationmodel 97 giving a corrected reference restitution image 213 of the saidrestitution reference 209, a parameterizable reverse restitutiontransformation model 297 producing the said restitution reference 209from the said corrected reference restitution image 213. Thisorganizational diagram also employs a mathematical restitutionprojection 96 giving a synthetic restitution image 307 of the correctedreference restitution image 213. The said synthetic restitution image307 exhibits a restitution deviation 214 compared with the restitutedreference 211.

DEFINITIONS AND DETAILED DESCRIPTION

Other characteristics and advantages of the invention will becomeapparent on reading:

-   -   of the definitions explained hereinafter of the employed        technical terms, referring to the indicative and non-limitative        examples of FIGS. 1 to 17,    -   of the description of FIGS. 1 to 17.

Scene

Scene 3 is defined as a place in three-dimensional space, containingobjects 107 illuminated by light sources.

Image-capture Appliance, Image, Image Capture

Referring to FIGS. 3 and 7, a description will now be given of what isunderstood by image-capture appliance 1 and image 103. Image-captureappliance 1 is defined as an appliance composed of an optical system100, of one or more sensors 101, of an electronic unit 102 and of amemory zone 16. By means of the said image-capture appliance 1, it ispossible to obtain, from a scene 3, fixed or animated digital images 103recorded in memory zone 16 or transmitted to an external device.Animated images are composed of a succession of fixed images 103 intime. The said image-capture appliance 1 can have the form in particularof a photographic appliance, of a video camera, of a camera connected toor integrated in a PC, of a camera connected to or integrated in apersonal digital assistant, of a camera connected to or integrated in atelephone, of a videoconferencing appliance or of a measuring camera orappliance sensitive to wavelengths other than those of visible light,such as a thermal camera.

Image capture is defined as the method by which image 103 is calculatedby image-capture appliance 1.

In the case in which an appliance is equipped with a plurality ofinterchangeable subassemblies, especially an optical system 100,image-capture appliance 1 is defined as a special configuration of theappliance.

Image-restitution Means, Restituted Image, Image Restitution

Referring to FIG. 2, a description will now be given of what isunderstood by image-restitution means 19. Such an image-restitutionmeans 19 can have the form in particular of a visual display screen, ofa television screen, of a flat screen, of a projector, of virtualreality goggles, of a printer.

Such an image-restitution means 19 is composed of:

-   -   an electronic unit,    -   one or more sources of light, of electrons or of ink,    -   one or more modulators: devices for modulation of light, of        electrons or of ink,    -   a focusing device, having in particular the form of an optical        system in the case of a light projector or the form of        electron-beam focusing coils in the case of a CRT screen, or the        form of filters in the case of a flat screen,    -   a restitution medium 190 having in particular the form of a        screen in the case of a CRT screen, of a flat screen or of a        projector, the form of a print medium on which printing is        performed in the case of a printer, or the form of a virtual        surface in space in the case of a virtual-image projector.

By means of the said image-restitution means 19, it is possible toobtain, from an image 103, a restituted image 191 on restitution medium190.

Animated images are composed of a succession of fixed images in time.

Image restitution is defined as the method by which the image isdisplayed or printed by means of image restitution means 19.

In the case in which a restitution means 19 is equipped with a pluralityof interchangeable subassemblies or of subassemblies that can be shiftedrelative to one another, especially restitution medium 190,image-restitution means 19 is defined as a special configuration.

Sensor Surface, Optical Center, Focal Distance

Referring to FIG. 1, a description will now be given of what is definedas sensor surface 110.

Sensor surface 110 is defined as the shape in space drawn by thesensitive surface of sensor 101 of image-capture appliance 1 at themoment of image capture. This surface is generally plane.

An optical center 111 is defined as a point in space associated withimage 103 at the moment of image capture. A focal distance is defined asthe distance between this point 111 and plane 110, in the case in whichsensor surface 110 is plane.

Pixel, Pixel Value, Exposure Time

Referring to FIG. 3, a description will now be given of what isunderstood by pixel 104 and pixel value.

A pixel 104 is defined as an elemental zone of sensor surface 110obtained by creating a grid, generally regular, of the said sensorsurface 110. Pixel value is defined as a number associated with thispixel 104.

Image capture is defined as determining the value of each pixel 104. Theset of these values constitutes image 103.

During image capture, the pixel value is obtained by integration, overthe surface of pixel 104, during a time period defined as exposure time,of part of the light flux derived from scene 3 via optical system 100,and by converting the result of this integration to a digital value. Theintegration of the light flux and/or the conversion of the result ofthis integration to a digital value are performed by means of electronicunit 102.

This definition of the concept of pixel value is applicable to the caseof black-and-white or color images 103, whether they be fixed oranimated.

Depending on the cases, however, the part in question of the light fluxis obtained in various ways:

a) In the case of a color image 103, sensor surface 110 is generallycomposed of a plurality of types of pixels 104, associated respectivelywith light fluxes of different wavelengths, examples being red, greenand blue pixels.

b) In the case of a color image 103, there may also be a plurality ofsensors 101 disposed side-by-side, each receiving part of the lightflux.

c) In the case of a color image 103, the colors used may be differentfrom red, green and blue, such as for North American NTSC television,and they may exceed three in number.

d) Finally, in the case of an interlaced television scanning camera, theanimated images produced are composed of an alternation of images 103containing even-numbered lines and of images 103 containing odd-numberedlines.

Configuration Used, Adjustments Used, Characteristics Used

The configuration used is defined as the list of removable subassembliesof image-capture appliance 1, such as optical system 100 which, if it isinterchangeable, is mounted on image-capture appliance 1. Theconfiguration used is characterized in particular by:

-   -   the type of optical system 100,    -   the serial number of optical system 100 or any other        designation.

Adjustments used are defined as:

-   -   the configuration used as defined hereinabove, as well as    -   the value of the manual or automatic adjustments available in        the configuration used and having an impact on the content of        image 103. These adjustments may be made by the user, especially        by means of pushbuttons, or may be calculated by image-capture        appliance 1. These adjustments may be stored in the appliance,        especially on a removable medium, or on any device connected to        the appliance. These adjustments may include in particular the        adjustments of focusing, diaphragm and focal length of optical        system 100, the adjustments of exposure time, the adjustments of        white balance, and the integrated image-processing adjustments,        such as digital zoom, compression and contrast.

Characteristics 74 used or set of characteristics 74 used are definedas:

a) Parameters related to the intrinsic technical characteristics ofimage-capture appliance 1, determined during the phase of design ofimage-capture appliance 1. For example, these parameters may include theformula of optical system 100 of the configuration used, which impactsthe geometric defects and the sharpness of the captured images; theformula of optical system 100 of the configuration used includes inparticular the shape, the arrangement and the material of the lenses ofoptical system 100.

These parameters may additionally include:

-   -   the geometry of sensor 101, or in other words sensor surface 110        as well as the shape and relative arrangement of pixels 104 on        this surface,    -   the noise generated by electronic unit 102,    -   the equation for conversion of light flux to pixel value.

b) Parameters associated with the intrinsic technical characteristics ofimage-capture appliance 1, determined during the phase of manufacture ofimage-capture appliance 1 and, in particular:

-   -   the exact positioning of the lenses in optical system 100 of the        configuration used,    -   the exact positioning of optical system 100 relative to sensor        101.

c) Parameters associated with the technical characteristics ofimage-capture appliance 1, determined at the moment of capture of image103 and, in particular:

-   -   the position and orientation of sensor surface 110 relative to        scene 3,    -   the adjustments used,    -   the external factors, such as temperature, if they have an        influence.

d) The user's preferences, especially the color temperature to be usedfor image restitution. For example, these preferences are selected bythe user by means of pushbuttons.

Observation Point, Observation Direction

Referring to FIG. 1, a description will now be given of what isunderstood by observation point 105 and observation direction 106.

Mathematical surface 10 is defined as a surface that is geometricallyassociated with sensor surface 110. For example, if the sensor surfaceis plane, it will be possible for mathematical surface 10 to coincidewith the sensor surface.

Observation direction 106 is defined as a line passing through at leastone point of scene 3 and through optical center 111. Observation point105 is defined as the intersection of observation direction 106 andsurface 10.

Observed Color, Observed Intensity

Referring to FIG. 1, a description will now be given of what isunderstood by observed color and observed intensity. Observed color isdefined as the color of the light emitted, transmitted or reflected bythe said scene 3 in the said observation direction 106 at a giveninstant, and observed from the said observation point 105. Observedintensity is defined as the intensity of the light emitted by the saidscene 3 in the said observation direction 106 at the same instant, andobserved from the said observation point 105.

The color can be characterized in particular by a light intensity thatis a function of wavelength, or else by two values as measured by acalorimeter. The intensity can be characterized by a value such asmeasured with a photometer.

The said observed color and the said observed intensity depend inparticular on the relative position of objects 107 in scene 3 and on theillumination sources present as well as on the transparency andreflection characteristics of objects 107 at the moment of observation.

Mathematical Projection, Mathematical Image, Mathematical Point,Mathematical Color of a Point, Mathematical Intensity of a Point,Mathematical Shape of a Point. Mathematical Position of a Point

Referring in particular to FIGS. 1, 5, 9 a, 9 b, 9 c and 9 d, adescription will be given of the concepts of mathematical projection 8,mathematical image 70, mathematical point, mathematical color of apoint, mathematical intensity of a point, mathematical shape 41 of apoint, and mathematical position 40 of a point.

Referring to FIG. 5, a description will now be given of how amathematical image 70 is constructed by specified mathematicalprojection 8 of at least one scene 3 on mathematical surface 10.

Firstly, a description will be given of what is understood by specifiedmathematical projection 8.

A specified mathematical projection 8 associates a mathematical image 70with:

-   -   a scene 3 at the moment of capture of an image 103,    -   and with the characteristics 74 used.

A specified mathematical projection 8 is a transformation with which thecharacteristics of each point of mathematical image 70 can be determinedfrom scene 3 at the moment of image capture and from the characteristics74 used.

Mathematical projection 8 is preferentially defined in the manner to bedescribed hereinafter.

Mathematical position 40 of the point is defined as the position ofobservation point 105 on mathematical surface 10.

Mathematical shape 41 of the point is defined as the geometric,punctiform shape of observation point 105.

Mathematical color of the point is defined as the observed color.

Mathematical intensity of the point is defined as the observedintensity.

Mathematical point is defined as the association of mathematicalposition 40, mathematical shape 41, mathematical color and mathematicalintensity for the observation point 105 under consideration.Mathematical image 70 is composed of the set of said mathematicalpoints.

The mathematical projection 8 of scene 3 is mathematical image 70.

Real Projection, Real Point, Real Color of a Point, Real Intensity of aPoint, Real Shape of a Point, Real Position of a Point

Referring in particular to FIGS. 3, 5, 9 a, 9 b, 9 c and 9 d, adescription will be given hereinafter of the concepts of real projection72, real point, real color of a point, real intensity of a point, realshape 51 of a point, and real position 50 of a point.

During image capture, image-capture appliance 1 associates an image 103of scene 3 with the characteristics 74 used. The light originating fromscene 3 in an observation direction 106 passes through optical system100 and arrives at sensor surface 110.

For the said observation direction, there is then obtained what isdefined as a real point, which exhibits differences compared with themathematical point.

Referring to FIGS. 9 a to 9 d, a description will now be given of thedifferences between the real point and the mathematical point.

The real shape 51 associated with the said observation direction 106 isnot a point on the sensor surface, but it has the form of a cloud inthree-dimensional space, where it has an intersection with one or morepixels 104. These differences are due in particular to coma, sphericalaberration, astigmatism, grouping into pixels 104, chromatic aberration,depth of field, diffraction, parasitic reflections and field curvatureof image-capture appliance 1. They give an impression of blurring, or oflack of sharpness of image 103.

In addition, real position 50 associated with the said observationdirection 106 exhibits a difference compared with mathematical position40 of a point. This difference is due in particular to the geometricdistortion, which gives an impression of deformation: for example,vertical walls appear to be curved. It is also due to the fact that thenumber of pixels 104 is limited, and that consequently the real position50 can have only a finite number of values.

In addition, the real intensity associated with the said observationdirection 106 exhibits differences compared with the mathematicalintensity of a point. These differences are due in particular to gammaand vignetting: for example, the edges of image 103 appear to be darker.Furthermore, noise may be added to the signal.

Finally, the real color associated with the said observation direction106 exhibits differences compared with the mathematical color of apoint. These differences are due in particular to gamma and the colorcast. Furthermore, noise may be added to the signal.

A real point is defined as the association of the real position 50, thereal shape 51, the real color and the real intensity for the observationdirection 106 under consideration.

The real projection 72 of scene 3 is composed of the set of real points.

Parameterizable Transformation Model, Parameters, Corrected Image

A parameterizable transformation model 12 (or parameterizabletransformation 12 for short) is defined as a mathematical transformationin which a corrected image 71 can be obtained from an image 103 and fromthe value of parameters. As indicated hereinbelow, the said parameterscan in particular be calculated from the characteristics 74 used.

By means of the said parameterizable transformation 12, it is possiblein particular to determine, for each real point of image 103, thecorrected position of the said real point, the corrected color of thesaid real point, the corrected intensity of the said real point, and thecorrected shape of the said real point, from the value of theparameters, from the real position of the said real point and from thevalues of the pixels of image 103. As an example, the corrected positioncan be calculated by means of polynomials of fixed degree as a functionof the real position, the coefficients of the polynomials depending onthe value of the parameters. The corrected color and the correctedintensity can be, for example, weighted sums of the values of thepixels, the coefficients depending on the value of the parameters and onthe real position, or else can be nonlinear functions of the values ofthe pixels of image 103.

A parameterizable reverse transformation model 212 (or parameterizablereverse transformation 212 for short) is defined as a mathematicaltransformation in which an image 103 can be obtained from a correctedimage 71 and from the value of parameters. The said parameters can becalculated in particular from the characteristics 74 used as indicatedhereinbelow.

By means of the said parameterizable reverse transformation 212, it ispossible in particular to determine, for each point of the correctedimage 71, the real point of image 103 corresponding to the said point ofcorrected image 71, and in particular the position of the said realpoint, the color of the said real point, the intensity of the said realpoint, and the shape of the said real point, from the value of theparameters and from corrected image 71. As an example, the position ofthe real point can be calculated by means of polynomials of fixed degreeas a function of the position of the point of the corrected image 71,the coefficients of the polynomials depending on the value of theparameters.

The parameters can include in particular: the focal length of opticalsystem 100 of the configuration used, or a related value such as theposition of a group of lenses, the focusing of optical system 100 of theconfiguration used, or a related value such as the position of a groupof lenses, the aperture of optical system 100 of the configuration used,or a related value such as the position of the diaphragm.

Difference Between the Mathematical Image and the Corrected Image

Referring to FIG. 5, the difference 73 between mathematical image 70 andcorrected image 71 for a given scene 3 and given characteristics 74 usedis defined as one or more values determined from numbers characterizingthe position, color, intensity, and shape of all or part of thecorrected points and of all or part of the mathematical points.

For example, the difference 73 between mathematical image 70 andcorrected image 71 for a given scene 3 and given characteristics 74 usedcan be determined as follows:

-   -   There are chosen characteristic points which, for example, may        be the points of an orthogonal array 80 of regularly disposed        points, as illustrated in FIG. 10.    -   The difference 73 is calculated, for example, by taking, for        each characteristic point, the sum of the absolute values of the        differences between each number characterizing the corrected        position, the corrected color, the corrected intensity and the        corrected shape respectively for the real point and for the        mathematical point. The sum function of the absolute values of        the differences may be replaced by another function such as the        mean, the sum of the squares or any other function with which        the numbers can be combined.

Reference Scene

A reference scene 9 is defined as a scene 3 for which certaincharacteristics are known. As an example, FIG. 4 a shows a referencescene 9 composed of a paper sheet bearing regularly disposed, solidblack circles. FIG. 4 b shows another paper sheet bearing the samecircles, with the addition of colored lines and areas. The circles areused to measure the real position 50 of a point, the lines to measurethe real shape 51 of a point, and the colored areas to measure the realcolor of a point and the real intensity of a point. This reference scene9 may be composed of a material other than paper.

Reference Image

Referring to FIG. 12, a definition will now be given of the concept ofreference image 11. A reference image 11 is defined as an image ofreference scene 9 obtained with image-capture appliance 1.

Synthetic Image, Synthetic-image Class

Referring to FIG. 12, a definition will now be given of the concept ofsynthetic image 207 and of synthetic-image class 7. A synthetic image207 is defined as a mathematical image 70 obtained by mathematicalprojection 8 of a reference scene 9. A synthetic-image class 7 isdefined as a set of mathematical images 70 obtained by mathematicalprojection 8 of one or more reference scenes 9 for one or more sets ofcharacteristics 74 used. In the case in which there is only onereference scene 9 and only one set of characteristics 74 used, thesynthetic-image class 7 comprises only one synthetic image 207.

Transformed Image

Referring to FIG. 12, a definition will now be given of the concept oftransformed image 13. A transformed image 13 is defined as the correctedimage obtained by application of a parameterizable transformation model12 to a reference image 11.

Transformed Image Close to a Synthetic-image Class, Deviation

Referring to FIG. 12, a description will now be given of the concept oftransformed image 13 close to a synthetic-image class 7 and of theconcept of deviation 14.

The difference between a transformed image 13 and a synthetic-imageclass 7 is defined as the smallest difference between the saidtransformed image 13 and any one of the synthetic images 207 of the saidsynthetic-image class 7.

Referring to FIG. 12, a description will next be given of a fourthalgorithm with which it is possible to choose, among the parameterizabletransformation models 12, that with which each reference image 11 can betransformed to a transformed image 13 close to the synthetic-image class7 of the reference scene 9 corresponding to the said reference image 11,in different cases of reference scenes 9 and characteristics 74 used.

-   -   In the case of a given reference scene 9 associated with a set        of given characteristics 74 used, there is chosen the        parameterizable transformation 12 (and its parameters) with        which the reference image 11 can be transformed to the        transformed image 13 that exhibits the smallest difference        compared with synthetic-image class 7. Synthetic-image class 7        and transformed image 13 are then said to be close. Deviation 14        is defined as the said difference.    -   In the case of a group of given reference scenes associated with        sets of given characteristics 74 used, the parameterizable        transformation 12 (and its parameters) is chosen as a function        of the differences between the transformed image 13 of each        reference scene 9 and the synthetic-image class 7 of each        reference scene 9 under consideration. There is chosen the        parameterizable transformation 12 (and its parameters) with        which the reference images 11 can be transformed to transformed        images 13 such that the sum of the said differences is        minimized. The sum function may be replaced by another function        such as the product. Synthetic-image class 7 and transformed        images 13 are then said to be close. Deviation 14 is defined as        a value obtained from the said differences, for example by        calculating the mean thereof.    -   In the case in which certain characteristics 74 used are        unknown, it is possible to determine them from the capture of a        plurality of reference images 11 of at least one reference scene        9. In this case, there are simultaneously determined the unknown        characteristics and the parameterizable transformation 12 (and        its parameters) with which the reference images 11 can be        transformed to transformed images 13, such that the sum of the        said differences is minimized, in particular by iterative        calculation or by solving equations concerning the sum of the        said differences and/or their product and/or any other        appropriate combination of the said differences. Synthetic-image        class 7 and transformed images 13 are then said to be close. The        unknown characteristics may be, for example, the relative        positions and orientations of sensor surface 110 and of each        reference scene 9 under consideration. Deviation 14 is defined        as a value obtained from the said differences, for example by        calculating the mean thereof. Referring to FIG. 12, a        description will next be given of a first calculation algorithm        with which a choice can be made:    -   within a set of parameterizable transformation models,    -   within a set of parameterizable reverse transformation models,    -   within a set of synthetic images,    -   within a set of reference scenes and within a set of transformed        images.

This choice is based on:

-   -   a reference scene 9, and/or    -   a transformed image 13, and/or    -   a parameterizable transformation model 12 with which the        reference image 11 obtained by capturing reference scene 9 by        means of image-capture appliance 1 can be transformed to        transformed image 13, and/or    -   a parameterizable reverse transformation model 212 with which        transformed image 13 can be transformed to reference image 11,        and/or    -   a synthetic image 207 obtained from reference scene 9 and/or        obtained from reference image 11.

The choice adopted is that which minimizes the difference betweentransformed image 13 and synthetic image 207. Synthetic image 207 andtransformed image 13 are then said to be close. Deviation 14 is definedas the said difference.

Preferably, according to the invention, it is possible by means of thefirst calculation algorithm to choose, within a set of mathematicalprojections, one mathematical projection 8 with which synthetic image207 can be constructed from reference scene 9.

Referring to FIG. 12, a description will next be given of a secondcalculation algorithm that includes the stages of:

-   -   choosing at least one reference scene 9,    -   capturing at least one reference image 11 of each reference        scene 9 by means of image-capture appliance 1.

This second algorithm additionally includes the stage of choosing,within a set of parameterizable transformation models and within a setof synthetic images:

-   -   a parameterizable transformation model 12 with which reference        image 11 can be transformed to a transformed image 13, and/or    -   a synthetic image 207 obtained from reference scene 9 and/or        obtained from reference image 11,

The choice adopted is that which minimizes the difference betweentransformed image 13 and synthetic image 207. Synthetic image 207 andtransformed image 13 are then said to be close. Deviation 14 is definedas the said difference.

Preferably, according to the invention, it is possible by means of thesecond calculation algorithm to choose, within a set of mathematicalprojections, one mathematical projection 8 with which synthetic image207 can be constructed from reference scene 9.

Best Transformation

The best transformation is defined as:

-   -   the transformation with which, among the parameterizable        transformation models 12, each reference image 11 can be        transformed to a transformed image 13 close to synthetic-image        class 7 of the reference scene 9 corresponding to the said        reference image 11, and/or,    -   the parameterizable transformation models 12 among which the        parameterizable transformation models, such as the transformed        image 13, are close to synthetic image 207, and/or    -   the parameterizable reverse transformation models 212 among        which the parameterizable reverse models, such as the        transformed image 13, are close to the synthetic image 207.

Calibration

Calibration is defined as a method with which data related to theintrinsic characteristics of image-capture appliance 1 can be obtained,for one or more configurations used, each composed of an optical system100 associated with an image-capture appliance 1.

Case 1: in the case in which there is only one configuration, the saidmethod includes the following stages:

-   -   the stage of mounting the said optical system 100 on the said        image-capture appliance 1,    -   the stage of choosing one or more reference scenes 9,    -   the stage of choosing several characteristics 74 used,    -   the stage of capturing images of the said reference scenes 9 for        the said characteristics used,    -   the stage of calculating the best transformation for each group        of reference scenes 9 corresponding to the same characteristics        74 used.

Case 2: in the case in which all the configurations corresponding to agiven image-capture appliance 1 and to all optical systems 100 of thesame type are taken into consideration, the said method includes thefollowing stages:

-   -   the stage of choosing one or more reference scenes 9,    -   the stage of choosing several characteristics 74 used,    -   the stage of calculating images 103 from characteristics 74 used        and in particular from formulas for optical system 100 of the        configuration used and from values of parameters, by means, for        example, of software for calculating the optical system by ray        tracing,    -   the stage of calculating the best transformation for each group        of reference scenes 9 corresponding to the same characteristics        used.

Case 3: in the case in which all the configurations corresponding to agiven optical system 100 and to all the image-capture appliances 1 ofthe same type are taken into consideration, the said method includes thefollowing stages:

-   -   the stage of mounting the said optical system 100 on an        image-capture appliance 1 of the type under consideration,    -   the stage of choosing one or more reference scenes 9,    -   the stage of choosing several characteristics 74 used,    -   the stage of capturing images of the said reference scenes 9 for        the said characteristics used,    -   the stage of calculating the best transformation for each group        of reference scenes 9 corresponding to the same characteristics        used.

Calibration can be performed preferentially by the manufacturer ofimage-capture appliance 1, for each appliance and configuration incase 1. This method is more precise but imposes more limitations and ishighly suitable in the case in which optical system 100 is notinterchangeable.

Alternatively, calibration can be performed by the manufacturer ofimage-capture appliance 1, for each appliance type and configuration incase 2. This method is less precise but is simpler.

Alternatively, calibration can be performed by the manufacturer ofimage-capture appliance 1 or by a third party, for each optical system100 and type of appliance in case 3. This method is a compromise inwhich one optical system 100 can be used on all image-capture appliances1 of one type, without repeating the calibration for each combination ofimage-capture appliance 1 and optical system 100. In the case in whichan image-capture appliance has a non-interchangeable optical system, themethod permits the calibration to be performed only one time for a giventype of appliance.

Alternatively, calibration can be performed by the appliance seller orinstaller, for each image-capture appliance 1 and configuration in case1.

Alternatively, calibration can be performed by the appliance seller orinstaller, for each optical system 100 and type of appliance in case 3.

Alternatively, calibration can be performed by the appliance user, foreach appliance and configuration in case 1.

Alternatively, calibration can be performed by the appliance user, foreach optical system 100 and type of appliance in case 3.

Design of the Digital Optical System

Design of the digital optical system is defined as a method for reducingthe cost of optical system 100, by:

-   -   designing an optical system 100 having defects, especially in        positioning of real points, or choosing the same from a catalog,    -   reducing the number of lenses, and/or    -   simplifying the shape of the lenses, and/or    -   using less expensive materials, processing operations or        manufacturing processes.

The said method includes the following stages:

-   -   the stage of choosing an acceptable difference (within the        meaning defined hereinabove),    -   the stage of choosing one or more reference scenes 9,    -   the stage of choosing several characteristics 74 used.

The said method also includes iteration of the following stages:

-   -   the stage of choosing an optical formula that includes in        particular the shape, material and arrangement of the lenses,    -   the stage of calculating images 103 from the characteristics 74        used and in particular from the formulas for optical system 100        of the configuration used, by employing, for example, software        for calculating the optical system by ray tracing, or by making        measurements on a prototype,    -   the stage of calculating the best transformation for each group        of reference scenes 9 corresponding to the same characteristics        74 used,    -   the stage of verifying if the difference is acceptable, until        the difference is acceptable.

Formatted Information

Formatted information 15 associated with image 103, or formattedinformation 15, is defined as all or part of the following data:

-   -   data related to the intrinsic technical characteristics of        image-capture appliance 1, especially the distortion        characteristics, and/or    -   data related to the technical characteristics of image-capture        appliance 1 at the moment of image capture, especially the        exposure time, and/or    -   data related to the preferences of the said user, especially the        color temperature, and/or    -   data related to the deviations 14.

Database of Characteristics

A database 22 of characteristics is defined as a database containingformatted information 15 for one or more image-capture appliances 1 andfor one or more images 103.

The said database 22 of characteristics can be stored in centralized ordistributed manner, and in particular can be:

-   -   integrated into image-capture appliance 1,    -   integrated into optical system 100,    -   integrated into a removable storage device,    -   integrated into a PC or other computer connected to the other        elements during image capture,    -   integrated into a PC or other computer connected to the other        elements after image capture,    -   integrated into a PC or other computer capable of reading a        storage medium shared with image-capture appliance 1,    -   integrated into a remote server connected to a PC or other        computer, itself connected to the other image-capture elements.

Fields

Referring to FIG. 8, a definition will now be given of the concept offields 91. The formatted information 15 associated with image 103 can berecorded in several forms and structured into one or more tables, but itcorresponds logically to all or part of fields 91, comprising:

(a) the focal distance,

(b) the depth of field

(c) the geometric defects.

The said geometric defects include geometric defects of image 103characterized by the parameters associated with the filmingcharacteristics 74 and a parameterizable transformation representing thecharacteristics of image-capture appliance 1 at the moment of filming.By means of the said parameters and of the said parameterizabletransformation, it is possible to calculate the corrected position of apoint of image 103.

The said geometric defects also include the vignetting characterized bythe parameters associated with filming characteristics 74 and aparameterizable transformation representing the characteristics ofimage-capture appliance 1 at the moment of filming. By means of the saidparameters and the said parameterizable transformation, it is possibleto calculate the corrected intensity of a point of image 103.

The said geometric defects also include the color cast characterized bythe parameters associated with filming characteristics 74 and aparameterizable transformation representing the characteristics ofimage-capture appliance 1 at the moment of filming. By means of the saidparameters and the said parameterizable transformation, it is possibleto calculate the corrected color of a point of image 103.

The said fields 91 also include (d) the sharpness of image 103.

The said sharpness includes the blurring in resolution of image 103characterized by the parameters associated with filming characteristics74 and a parameterizable transformation representing the characteristicsof image-capture appliance 1 at the moment of filming. By means of thesaid parameters and the said parameterizable transformation, it ispossible to calculate the corrected shape of a point of image 103.Blurring covers in particular coma, spherical aberration, astigmatism,grouping into pixels 104, chromatic aberration, depth of field,diffraction, parasitic reflections and field curvature.

The said sharpness also includes the blurring in depth of field, inparticular spherical aberrations, coma and astigmatism. The saidblurring depends on the distance of the points of scene 3 relative toimage-capture appliance 1, and it is characterized by the parametersassociated with filming characteristics 74 and a parameterizabletransformation representing the characteristics of image-captureappliance 1 at the moment of filming. By means of the said parametersand of the said parameterizable transformation, it is possible tocalculate the corrected shape of a point of image 103.

The said fields 91 also include (e) parameters of the quantizationmethod. The said parameters depend on the geometry and physics of sensor101, on the architecture of electronic unit 102 and on any processingsoftware that may be used.

The said parameters include a function that represents the variations ofintensity of a pixel 104 as a function of wavelength and light fluxderived from the said scene 3. The said function includes in particulargamma information.

The said parameters also include:

-   -   the geometry of the said sensor 101, especially the shape, the        relative position and the number of sensitive elements of the        said sensor 101,    -   a function representative of the spatial and temporal        distribution of noise of image-capture appliance 1,    -   a value representative of the exposure time for image capture.

The said fields 91 also include (f) parameters of the digital-processingoperations performed by image-capture appliance 1, especially digitalzoom and compression. These parameters depend on the processing softwareof image-capture appliance 1 and on the user's adjustments.

The said fields 91 also include:

(g) parameters representative of the user's preferences, especially asregards the degree of blurring and the resolution of image 103.

(h) the deviations 14.

Calculation of Formatted Information

The formatted information 15 can be calculated and recorded in database22 in several stages.

a) A stage at the end of design of image-capture appliance 1.

By means of this stage it is possible to obtain intrinsic technicalcharacteristics of image-capture appliance 1, and in particular:

-   -   the spatial and temporal distribution of the noise generated by        electronic unit 102,    -   the formula for conversion of light flux to pixel value,    -   the geometry of sensor 101.

b) A stage at the end of calibration or design of the digital opticalsystem.

By means of this stage it is possible to obtain other intrinsictechnical characteristics of image-capture appliance 1, and inparticular, for a certain number of values of characteristics used, thebest associated transformation and the associated deviation 14.

c) A stage in which the user's preferences are chosen by means ofpushbuttons, menus or removable media, or of connection to anotherdevice.

d) An image capture stage.

By means of this stage (d) it is possible to obtain technicalcharacteristics of image-capture appliance 1 at the moment of imagecapture, and in particular the exposure time, which is determined by themanual or automatic adjustments made.

By means of stage (d) it is also possible to obtain the focal distance.The focal distance is calculated from:

-   -   a measurement of the position of the group of lenses of variable        focal length of optical system 100 of the configuration used, or    -   a set value input to the positioning motor, or    -   a manufacturer's value if the focal length is fixed.

The said focal distance can then be determined by analysis of thecontent of image 103.

By means of stage (d) it is also possible to obtain the depth of field.The depth of field is calculated from:

-   -   a measurement of the position of the group of focusing lenses of        optical system 100 of the configuration used, or    -   a set value input to the positioning motor, or    -   a manufacturer's value if the depth of field is fixed.

By means of stage (d) it is also possible to obtain the defects ofgeometry and of sharpness. The defects of geometry and of sharpnesscorrespond to a transformation calculated by means of a combination oftransformations of the database 22 of characteristics obtained at theend of stage (b). This combination is chosen to represent the values ofparameters corresponding to the characteristics 74 used, especially thefocal distance.

By means of stage (d) it is also possible to obtain the parameters ofdigital processing performed by image-capture appliance 1. Theseparameters are determined by the manual or automatic adjustments made.

The calculation of formatted information 15 according to stages (a) to(d) can be performed by:

-   -   a device or software integrated into image-capture appliance 1,        and/or    -   driver software in a PC or other computer, and/or    -   software in a PC or other computer, and/or    -   a combination of the three.

The foregoing transformations in stage (b) and stage (d) can be storedin the form of:

-   -   a general mathematical formula,    -   a mathematical formula for each point,    -   a mathematical formula for certain characteristic points.

The mathematical formulas can be described by:

-   -   a list of coefficients,    -   a list of coefficients and coordinates.

By means of these different methods it is possible to reach a compromisebetween the size of the memory available for storage of the formulas andthe calculating power available for calculation of the corrected images71.

In addition, in order to retrieve the data, identifiers associated withthe data are recorded in database 22. These identifiers include inparticular:

-   -   an identifier of the type and of the reference of image-capture        appliance 1,    -   an identifier of the type and of the reference of optical system        100, if it is removable,    -   an identifier of the type and of the reference of any other        removable element having a link to the stored information,    -   an identifier of image 103,    -   an identifier of the formatted information 15.

Completed Image

As described by FIG. 11, a completed image 120 is defined as the image103 associated with the formatted information 15. This completed image120 can preferenatially bare the form of a file P100, as described byFIG. 14. Completed image 120 can also be distributed into a plurality offiles.

Completed image 120 can be calculated by image-capture appliance 1. Itcan also be calculated by an external calculating device, such as acomputer.

Image-processing Software

Image-processing software 4 is defined as software that accepts one ormore completed images 120 as input and that performs processingoperations on these images. These processing operations can include inparticular:

-   -   calculating a corrected image 71,    -   performing measurements in the real world,    -   combining several images,    -   improving the fidelity of the images relative to the real world,    -   improving the subjective quality of images,    -   detecting objects or persons 107 in a scene 3,    -   adding objects or persons 107 to a scene 3,    -   replacing or modifying objects or persons 107 in a scene 3,    -   removing shadows from a scene 3,    -   adding shadows to a scene 3,    -   searching for objects in an image base.

The said image processing software can be:

-   -   integrated into image-capture appliance 1,    -   run on calculating means 17 connected to image-capture appliance        1 by transmission means 18.

Digital Optical System

A digital optical system is defined as the combination of animage-capture appliance 1, a database 22 of characteristics and acalculating means 17 that permits:

-   -   image capture of an image 103,    -   calculation of the completed image,    -   calculation of the corrected image 71.

Preferentially, the user obtains corrected image 71 directly. If hewishes, the user may demand suppression of automatic correction.

The database 22 of characteristics may be:

-   -   integrated into image-capture appliance 1,    -   integrated into a PC or other computer connected to the other        elements during image capture,    -   integrated into a PC or other computer connected to the other        elements after image capture,    -   integrated into a PC or other computer capable of reading a        storage medium shared with image-capture appliance 1,    -   integrated into a remote server connected to a PC or other        computer, itself connected to the other image-capture elements.

Calculating means 17 may be:

-   -   integrated onto a component together with sensor 101,    -   integrated onto a component together with part of electronics        unit 102,    -   integrated into image-capture appliance 1,    -   integrated into a PC or other computer connected to the other        elements during image capture,    -   integrated into a PC or other computer connected to the other        elements after image capture,    -   integrated into a PC or other computer capable of reading a        storage medium shared with image-capture appliance 1,    -   integrated into a remote server connected to a PC or other        computer, itself connected to the other image-capture elements.

Processing of the Complete Chain

The foregoing paragraphs have essentially presented precise details ofthe concepts and description of the method and system according to theinvention for providing, to image-processing software 4, formattedinformation 15 related to the characteristics of image-capture appliance1.

In the paragraphs to follow, an expanded definition will be given of theconcepts and a supplemented description will be given of the method andsystem according to the invention for providing, to image-processingsoftware 4, formatted information 15 related to the characteristics ofimage-restitution means 19. In this way the processing of a completechain will be explained.

By means of the processing of the complete chain, it is possible:

-   -   to improve the quality of image 103 from one end of the chain to        the other, to obtain a restituted image 191 while correcting the        defects of image-capture appliance 1 and of image-restitution        means 19, and/or    -   to use optical systems of lower quality and of lower cost in a        video projector in combination with software for improvement of        image quality.

Definitions Associated with the Image-restitution Means

On the basis of FIGS. 2, 17 and 6, a description will now be given ofhow the characteristics of an image-restitution means 19 such as aprinter, a visual display screen or a projector are taken into accountin the formatted information 15.

The supplements or modifications to be made to the definitions in thecase of an image-restitution means 19 may be inferred by analogy by aperson skilled in the art by analogy with the definitions provided inthe case of an image-capture appliance 1. Nevertheless, in order toillustrate this method, a description with reference in particular toFIG. 6 and FIG. 17 will now be given of the main supplements ormodifications.

By restitution characteristics 95 used there are designated theintrinsic characteristics of image-restitution means 19, thecharacteristics of image-restitution means 19 at the moment of imagerestitution, and the user's preferences at the moment of imagerestitution. In the case of a projector in particular, the restitutioncharacteristics 95 used include the shape and position of the screenused.

By parameterizable restitution transformation model 97 (orparameterizable restitution transformation 97 for short), there isdesignated a mathematical transformation similar to parameterizabletransformation model 12. By parameterizable reverse restitutiontransformation model 297 (or parameterizable reverse restitutiontransformation 297 for short), there is designated a mathematicaltransformation similar to parameterizable reverse transformation model212.

By corrected restitution image 94 there is designated the image obtainedby application of parameterizable restitution transformation 97 to image103.

By mathematical restitution projection 96 there is designated amathematical projection that associates, with a corrected restitutionimage 94, a mathematical restitution image 92 on the mathematicalrestitution surface geometrically associated with the surface ofrestitution medium 190. The mathematical restitution points of themathematical restitution surface have a shape, position, color andintensity calculated from corrected restitution image 94.

By real restitution projection 90 there is designated a projection thatassociates a restituted image 191 with an image 103. The pixel values ofimage 103 are converted by the electronic unit of restitution means 19to a signal that drives the modulator of restitution means 19. Realrestitution points are obtained on restitution medium 190. The said realrestitution points are characterized by shape, color, intensity andposition. The phenomenon of grouping into pixels 104 describedhereinabove in the case of an image-capture appliance 1 does not occurin the case of an image-restitution means. On the other hand, an inversephenomenon occurs, with the result in particular that lines take on astaircase appearance.

Restitution difference 93 is designated as the difference betweenrestituted image 191 and mathematical restitution image 92. Thisrestitution difference 93 is obtained by analogy with difference 73.

By restitution reference 209 there is designated an image 103 in whichthe values of pixels 104 are known. By restituted reference 211 there isdesignated the restituted image 191 obtained by mathematical restitutionprojection 90 of restitution reference 209. By corrected referencerestitution image 213, there is designated the corrected restitutionimage 94 corresponding to restitution reference 209 for parameterizablerestitution transformation model 97 and/or for parameterizable reverserestitution transformation model 297. By synthetic restitution image 307there is designated the mathematical restitution image 92 obtained bymathematical restitution projection 96 of corrected referencerestitution image 213.

By best restitution transformation there is designated:

-   -   for a restitution reference 209 and the restitution        characteristics 95 used, that with which image 103 can be        transformed to a corrected restitution image 94 such that its        mathematical restitution projection 92 exhibits the minimum        restitution difference 93 compared with restituted image 191,        and/or    -   the parameterizable restitution transformation 97 among the        parameterizable restitution transformation models such that        restituted reference 211 exhibits the minimum restitution        difference 93 compared with the synthetic restitution image 307,        and/or    -   the parameterizable reverse restitution transformation 297 among        the parameterizable reverse transformation models such that the        restituted reference 211 exhibits the minimum restitution        difference 93 compared with the synthetic restitution image 307.

The restituted reference 211 and the synthetic restitution image 307 arethen said to be close.

The methods of restitution calibration and of design of the digitaloptical restitution system are comparable with the methods ofcalibration and of design of the digital optical system in the case ofan image-capture appliance 1. Nevertheless, differences are present incertain stages, and in particular the following stages:

-   -   the stage of choosing a restitution reference 209;    -   the stage of performing restitution of the said restitution        reference;    -   the stage of calculating the best restitution transformation.

Preferably, according to the invention, the method includes a sixthalgorithm for calculation of the formatted information 15. By means ofthis sixth algorithm it is possible to make a choice:

-   -   within a set of parameterizable restitution transformation        models,    -   within a set of parameterizable reverse restitution        transformation models,    -   within a set of mathematical restitution projections,    -   within a set of restitution references and within a set of        corrected reference restitution images.

The choice made by this sixth algorithm is based on:

-   -   a restitution reference 209, and/or    -   a corrected reference restitution image 213, and/or    -   a parameterizable restitution transformation model 97 with which        the restitution reference 209 can be transformed to the        corrected reference restitution image 213, and/or    -   a parameterizable reverse restitution transformation model 297        with which the corrected reference restitution image 213 can be        transformed to the restitution reference 209, and/or    -   a mathematical restitution projection 96 with which a synthetic        restitution image 307 can be constructed from the corrected        reference restitution image 213.

The choice is made by this sixth algorithm in such a way that thesynthetic restitution image 307 is close to the restituted reference 211obtained by restitution of restitution reference 209 by means ofimage-restitution means 19. Restituted reference 211 exhibits arestitution deviation 214 compared with synthetic restitution image 307.

According to an alternative embodiment of the invention, the methodincludes a seventh algorithm for calculation of the formattedinformation. This seventh algorithm includes the stages of:

-   -   choosing at least one restitution reference 209,    -   restituting restitution reference 209 to a restituted reference        211 by means of image-restitution means 19.

By means of this seventh algorithm it is also possible to choose, withina set of parameterizable restitution transformation models and within aset of mathematical restitution projections:

-   -   a parameterizable restitution transformation model 97 with which        restitution reference 209 can be transformed to a corrected        reference restitution image 213, and    -   a mathematical restitution projection 96 with which a synthetic        restitution image 307 can be constructed from corrected        reference restitution image 213.

The choice is made by the seventh algorithm in such a way that syntheticrestitution image 307 is close to restituted reference 211. Therestituted reference exhibits a restitution deviation 214 compared withthe synthetic restitution image 307. By means of parameterizable reverserestitution transformation model 297, it is possible to transformcorrected reference restitution image 213 to restitution reference 209.

According to another alternative embodiment of the invention, the methodincludes an eighth algorithm for calculation of the formattedinformation. This eighth algorithm includes the stage of choosing acorrected reference restitution image 213. This eighth algorithm alsoincludes the stage of making a choice within a set of parameterizablerestitution transformation models, within a set of mathematicalrestitution projections and within a set of restitution references. Thischoice is based on:

-   -   a restitution reference 209, and/or    -   a parameterizable restitution transformation model 97 with which        restitution reference 209 can be transformed to corrected        reference restitution image 213, and/or    -   a parameterizable reverse restitution transformation model 297        with which the corrected reference restitution image 213 can be        transformed to the restitution reference 209, and/or    -   a mathematical restitution projection 96 with which a synthetic        restitution image 307 can be constructed from the corrected        reference restitution image 213.

The eighth algorithm makes this choice in such a way that syntheticrestitution image 307 is close to restituted reference 211 obtained byrestitution of restitution reference 209, by means of image-restitutionmeans 19. Restituted reference 211 exhibits a restitution deviationcompared with synthetic restitution image 307.

Preferably, according to the invention, the method includes a ninthalgorithm for calculating the restitution deviations 214. This ninthalgorithm includes the stages of:

-   -   calculating the restitution deviations 214 between restituted        reference 211 and synthetic restitution image 307,    -   associating restitution deviations 214 with formatted        information 15.

It results from the combination of technical features that it ispossible to verify automatically, for example during manufacture of theappliance, that the method has produced formatted information withinacceptable tolerances.

The formatted information 15 related to an image-capture appliance 1 andthat related to an image-restitution means 19 can be used end-to-end forthe same image.

It is also possible to combine the formatted information 15 related toeach of the appliances to obtain formatted information 15 related to theappliance chain, for example by addition of a vector field, in the caseof geometric distortion.

In the foregoing, a description was given of the concept of field in thecase of an image-capture appliance 1. This concept is also applicable byanalogy in the case of image-restitution means 19. Nonetheless theparameters of the quantization method are replaced by the parameters ofthe signal-reconstitution method, meaning: the geometry of restitutionmedium 190 and its position, a function representing the spatial andtemporal distribution of the noise of image-restitution means 19.

In an alternative embodiment according to the invention, restitutionmeans 19 is associated with an image-capture appliance 1 to restitute,in digital form, restituted reference 211 from restitution reference209. The method is such that, to produce the formatted information 15related to the defects P5 of restitution means 19, the formattedinformation 15 related to image-capture appliance 1 associated with therestitution means is used, for example, to correct the defects ofimage-capture appliance 1 in such a way that restituted reference 211contains only the defects P5 of restitution means 19.

Generalization of the Concepts

The technical features of which the invention is composed and which arespecified in the claims have been defined, described and illustrated byreferring essentially to image-capture appliances of digital type, or inother words appliances that produce digital images. It can be easilyunderstood that the same technical features are applicable in the caseof image-capture appliances that would be the combination of anappliance based on silver technology (a photographic or cinematographicappliance using sensitive silver halide films, negatives or reversalfilms) with a scanner for producing a digital image from the developedsensitive films. Certainly it is appropriate in this case to adapt atleast some of the definitions used. Such adaptations are within thecapability of the person skilled in the art. In order to demonstrate theobvious character of such adaptations, it is merely necessary to mentionthat the concepts of pixel and pixel value illustrated by referring toFIG. 3 must, in the case of the combination of an appliance based onsilver technology with a scanner, be applied to an elemental zone of thesurface of the film after this has been digitized by means of thescanner. Such transpositions of definitions are self-evident and can beextended to the concept of the configuration used. As an example, thelist of removable subassemblies of image-capture appliance 1 included inthe configuration used can be supplemented by the type of photographicfilm effectively used in the appliance based on silver technology.

Other characteristics and advantages of the invention will become clearupon reading the indicative and non-limitative definitions and examplesexplained hereinafter with reference to FIGS. 1 to 17.

Appliance

Referring in particular to FIGS. 2, 3 and 13, a description will begiven of the concept of appliance P25. Within the meaning of theinvention, an appliance P25 may be in particular:

-   -   an image-capture appliance 1, such as a disposable photo        appliance, a digital photo appliance, a reflex appliance, a        scanner, a fax machine, an endoscope, a camcorder, a        surveillance camera, a game, a camera integrated into or        connected to a telephone, to a personal digital assistant or to        a computer, a thermal camera or an echographic appliance,    -   an image-restitution appliance 19 or image-restitution means 19,        such as a screen, a projector, a television set, virtual-reality        goggles or a printer,    -   an appliance, including its installation, such as a projector, a        screen and the manner in which they are positioned,    -   the positioning of an observer relative to an image-restitution        appliance 19, which introduces parallax errors in particular,    -   a human being or observer having vision defects, such as        astigmatism,    -   an appliance which it is hoped can be emulated, to produce        images having, for example, an appearance similar to those        produced by an appliance of the Leica brand,    -   an image-processing device, such as zoom software, which has the        edge effect of adding blurring,    -   a virtual appliance equivalent to a plurality of appliances P25,

A more complex appliance P25, such as a scanner/fax/printer, aphoto-printing Minilab, or a videoconferencing appliance can be regardedas an appliance P25 or as a plurality of appliances P25.

Appliance Chain

Referring in particular to FIG. 13, a description will now be given ofthe concept of appliance chain P3. An appliance chain P3 is defined as aset of appliances P25. The concept of appliance chain P3 may alsoinclude a concept of order.

The following examples constitute appliance chains P3:

-   -   a single appliance P25,    -   an image-capture appliance 1 and an image-restitution appliance        19,    -   a photo appliance, a scanner or a printer, for example in a        photo-printing Minilab,    -   a digital photo appliance or a printer, for example in a        photo-printing Minilab,    -   a scanner, a screen or a printer, for example in a computer,    -   a screen or projector, and the eye of a human being,    -   one appliance and another appliance which it is hoped can be        emulated,    -   a photo appliance and a scanner,    -   an image-capture appliance and image-processing software,    -   image-processing software and an image-restitution appliance 19,    -   a combination of the preceding examples,    -   another set of appliances P25.

Defect

Referring in particular to FIG. 13, a description will now be given ofthe concept of defect P5. A defect P5 of appliance P25 is defined as adefect related to the characteristics of the optical system and/or ofthe sensor and/or of the electronic unit and/or of the softwareintegrated in an appliance P25; examples of defects P5 include geometricdefects, sharpness defects, colorimetry defects, geometric distortiondefects, geometric chromatic aberration defects, geometric vignettingdefects, contrast defects, colorimetry defects, in particular renderingof colors and color cast, defects of flash uniformity, sensor noise,grain, astigmatism defects and spherical aberration defects.

Image

Referring in particular to FIGS. 2, 5, 6 and 13, a description will nowbe given of the concept of image 103. Image 103 is defined as a digitalimage captured or modified or restituted by an appliance P25. Image 103may originate from an appliance P25 of appliance chain P3. Image 103 maybe addressed to an appliance P25 of appliance chain P3. More generally,image 103 may be derived from and/or addressed to appliance chain P3. Inthe case of animated images, such as video images, composed of a timesequence of fixed images, image 103 is defined as one fixed image of thesequence of images.

Formatted Information

Referring in particular to FIGS. 7, 8, 10 and 13, a description will nowbe given of the concept of formatted information 15. Formattedinformation 15 is defined as data related to the defects P5 orcharacterizing the defects P5 of one or more appliances P25 of appliancechain P3 and enabling image-processing means P1 to modify the quality ofimages 103 by making allowance for the defects P5 of appliance P25.

To produce the formatted information 15, there can be used variousmethods and systems based on measurements and/or simulations and/orcalibrations, such as, for example, the calibration method describedhereinabove.

To transmit the formatted information 15, there can be used a file P100containing the completed image 120. As an example, an image-captureappliance 1 such as a digital photo appliance can produce filescontaining image 103, formatted information 15 copied from an internalmemory of the appliance, and data in Exif format containing theadjustments used.

To produce the formatted information 15, it is possible, for example, touse the method and the system described in the International PatentApplication filed on the same day as the present application in the nameof Vision IQ and entitled “Method and system for producing formattedinformation related to geometric distortions”. That applicationdescribes a method for producing formatted information 15 related to theappliances P25 of an appliance chain P3. Appliance chain P3 is composedin particular of at least one image-capture appliance 1 and/or at leastone image-restitution appliance 19. The method includes the stage ofproducing formatted information 15 related to the geometric distortionsof at least one appliance P25 of the chain.

Appliance P25 preferably makes it possible to capture or restitute animage on a medium. Appliance P25 contains at least one fixedcharacteristic and/or one variable characteristic depending on theimage. The fixed characteristic and/or variable characteristic can beassociated with one or more values of characteristics, especially thefocal length and/or the focusing and their values of associatedcharacteristics. The method includes the stage of producing, from ameasured field, measured formatted information related to the geometricdistortions of the appliance. The formatted information 15 may includethe measured formatted information.

To produce the formatted information 15, it is possible, for example, touse the method and the system described in the International PatentApplication filed on the same day as the present application in the nameof Vision IQ and entitled “Method and system for producing formattedinformation related to the defects of at least one appliance of a chain,especially to blurring”. That application describes a method forproducing formatted information 15 related to the appliances P25 of anappliance chain P3. Appliance chain P3 is composed in particular of atleast one image-capture appliance and/or at least one image-restitutionappliance 19. The method includes the stage of producing formattedinformation 15 related to the defects P5 of at least one appliance P25of the chain. Preferably, appliance P25 with which an image can becaptured or restituted contains at least one fixed characteristic and/orone variable characteristic depending on the image (I). The fixed and/orvariable characteristics can be associated with one or more values ofcharacteristics, especially the focal length and/or the focusing andtheir values of associated characteristics. The method includes thestage of producing measured formatted information related to the defectsP5 of appliance P25 from a measured field. The formatted information 15may include the measured formatted information.

To produce the formatted information 15, it is possible, for example, touse the method and the system described in the International PatentApplication filed on the same day as the present application in the nameof Vision IQ and entitled “Method and system for reducing updatefrequency of image processing means”. That application describes amethod for reducing the update frequency of image-processing means P1,in particular software and/or a component. The image-processing meansmake it possible to modify the quality of the digital images derivedfrom or addressed to an appliance chain P3. Appliance chain P3 iscomposed in particular of at least one image-capture appliance and/or atleast one image-restitution appliance 19. Image-processing means P1employ formatted information 15 related to the defects P5 of at leastone appliance of appliance chain P5. The formatted information 15depends on at least one variable. The formatted information 15 makes itpossible to establish a correspondence between one part of the variablesand of the identifiers. By means of the identifiers it is possible todetermine the value of the variable corresponding to the identifier bytaking the identifier and the image into account. It results from thecombination of technical features that it is possible to determine thevalue of a variable, especially in the case in which the physicalsignificance and/or the content of the variable are known only afterdistribution of image-processing means P1. It also results from thecombination of technical features that the time between two updates ofthe correction software can be spaced apart. It also results from thecombination of technical features that the various economic players thatproduce appliances and/or image-processing means can update theirproducts independently of other economic players, even if the latterradically change the characteristics of their product or are unable toforce their client to update their products. It also results from thecombination of technical features that a new functionality can bedeployed progressively by starting with a limited number of economicplayers and pioneer users.

To search for the formatted information 15, it is possible, for example,to use the method and the system described in the International PatentApplication filed on the same day as the present application in the nameof Vision IQ and entitled “Method and system for modifying the qualityof at least one image derived from or addressed to an appliance chain”.That application describes a method for modifying the quality of atleast one image 103 derived from or addressed to a specified appliancechain. The specified appliance chain is composed of at least oneimage-capture appliance and/or at least one image-restitution appliance19. The image-capture appliances and/or the image-restitution appliancesbeing progressively introduced on the market by separate economicplayers belong to an indeterminate set of appliances. The appliances P25of the set of appliances exhibit defects P5 that can be characterized byformatted information 15. For the image in question, the method includesthe following stages:

-   -   the stage of compiling directories of the sources of formatted        information related to the appliances P25 of the set of        appliances,    -   the stage of automatically searching for specific formatted        information related to the specified appliance chain among the        formatted information 15 compiled in this way,    -   the stage of automatically modifying the image by means of        image-processing software and/or image-processing components,        while taking into account the specific formatted information        obtained in this way.

To exploit the formatted information 15, it is possible, for example, touse the method and the system described in the International PatentApplication filed on the same day as the present application in the nameof Vision IQ and entitled “Method and system for calculating atransformed image from a digital image and formatted information relatedto a geometric transformation”. That application describes a method forcalculating a transformed image from a digital image and formattedinformation 15 related to a geometric transformation, especiallyformatted information 15 related to the distortions and/or chromaticaberrations of an appliance chain P3. The method includes the stage ofcalculating the transformed image from an approximation of the geometrictransformation. It results therefrom that the calculation is economicalin terms of memory resources, in memory bandpass, in calculating powerand therefore in electricity consumption. It also results therefrom thatthe transformed image does not exhibit any visible or annoying defect asregards its subsequent use.

To exploit the formatted information 15, it is possible, for example, touse the method and the system described in the International PatentApplication filed on the same day as the present application in the nameof Vision IQ and entitled “Method and system for modifying a digitalimage, taking into account its noise”. That application describes amethod for calculating a transformed image from a digital image andformatted information 15 related to the defects P5 of an appliance chainP3. Appliance chain P3 includes image-capture appliances and/orimage-restitution appliances. Appliance chain P3 contains at least oneappliance P25. The method includes the stage of automaticallydetermining the characteristic data from the formatted information 15and/or the digital image. It results from the combination of technicalfeatures that the transformed image does not exhibit any visible orannoying defect, especially defects related to noise, as regards itssubsequent use.

Image-processing Means

Referring in particular to FIGS. 7 and 13, a description will now begiven of the concept of image-processing means P1. Within the meaning ofthe present invention, image-processing means P1 are defined, forexample, as image-processing software 4 and/or a component and/or anequipment item and/or a system capable of modifying the quality of image103 by employing formatted information 15 in order to produce a modifiedimage, such as a corrected image 71 or a corrected restitution image 97.The modified image may be addressed to a second appliance of appliancechain P3, distinct or not from appliance P25, for example, the followingappliance in appliance chain P3.

The modification of image quality by image-processing means P1 mayconsist, for example, in:

-   -   suppressing or attenuating the defects P5 of one or more        appliances P25 of appliance chain P3 in image 103, and/or    -   modifying image 103 to add at least one defect P5 of one or more        appliances P25 of appliance chain P3 in such a way that the        modified image resembles an image captured by appliance or        appliances P25, and/or    -   modifying image 103 to add at least one defect P5 of one or more        appliances P25 of appliance chain P3 in such a way that the        restitution of the modified image resembles an image restituted        by appliance or appliances P25, and/or    -   modifying image 103 by taking into account the formatted        information 15 related to the vision defects P5 of the eye P25        of a human being in appliance chain P3 in such a way that        restitution of the modified image is perceived by the eye of the        human being as corrected for all or part of the defects P5.

A correction algorithm is defined as the method employed by animage-processing means P1 to modify image quality depending on thedefect P5.

Image-processing means P1 may assume various forms depending on theapplication.

Image-processing means P1 may be integrated entirely or partly inappliance P25, as in the following examples:

-   -   an image-capture appliance that produces modified images, such        as a digital photo appliance in which image-processing means P1        are integrated,    -   an image-restitution appliance 19, which displays or prints        modified images, such as a video projector in which        image-processing means P1 are included,    -   a hybrid appliance, which corrects the defects of its elements,        such as a scanner/printer/fax machine in which image-processing        means P1 are included,    -   a professional image-capture appliance, which produces modified        images, such as an endoscope in which image-processing means P1        are included.

In the case in which image-processing means P1 are integrated inappliance P25, appliance P25 in practice corrects its own defects P5,and the appliances P25 of appliance chain P3 can be determined bydesign, for example in a fax machine: a scanner and a printer;nevertheless, the user is able to use only part of the appliances P25 ofappliance chain P3, for example if the fax machine can also be used as astand-alone printer.

Image-processing means P1 can be integrated entirely: or partly in acomputer, for example in the following manner:

-   -   in an operating system, such as Windows or the Mac OS, in order        to modify automatically the quality of images derived from or        addressed to a plurality of appliances P25, which may vary        depending on image 103 and/or in time, examples being scanners,        photo appliances and printers; the automatic correction may be        made, for example, when image 103 is input into the system, or        when printing is requested by the user,    -   in an image-processing application, such as Photoshop™, to        modify automatically the quality of images derived from or        addressed to a plurality of appliances P25, which may vary        depending on image and/or in time, examples being scanners,        photo appliances and printers; the automatic correction may be        made, for example, when the user activates a filter command in        Photoshop™,    -   in a photo-printing appliance (such as Photofinishing or Minilab        in English), to modify automatically the quality of images        derived from a plurality of photo appliances, which may vary        depending on the image and/or in time, examples being disposable        cameras, digital photo appliances and compact disks, the        automatic correction may take into account the photo appliances        as well as the integrated scanner and printer, and may be        applied at the moment at which the printing jobs are initiated,    -   on a server, for example on the Internet, to modify        automatically the quality of images derived from a plurality of        photo appliances, which may vary depending on the image and/or        in time, examples being disposable cameras and digital photo        appliances, the automatic correction may take into account the        photo appliances as well as a printer, for example, and may be        applied at the moment at which the images 103 are recorded on        the server, or at the moment at which the printing jobs are        initiated.

In the case in which image-processing means P1 are integrated in acomputer, image-processing means P1 are for practical purposescompatible with multiple appliances P25, and at least one appliance P25of appliance chain P3 may vary from one image 103 to another.

To provide formatted information 15 in a standard format toimage-processing means P1, it is possible, for example, to associate theformatted information 15 with image 103:

-   -   in a file P100,    -   by using identifiers of appliances P25 of appliance chain P3,        such as data in Exif format in file P100, in order to retrieve        formatted information 15 in database 22 of characteristics.

Variable Characteristic

On the basis of FIG. 13, a description will now be given of the conceptof variable characteristic P6. According to the invention, a variablecharacteristic P6 is defined as a measurable factor, which is variablefrom one image 103 to another that has been captured, modified orrestituted by the same appliance P25, and which has an influence ondefect P5 of the image that has been captured, modified or restituted byappliance P25, especially:

-   -   a global variable characteristic, which is fixed for a given        image 103, an example being a characteristic of appliance P25 at        the moment of capture or restitution of the image, related to an        adjustment of the user or related to an automatic function of        appliance P25, such as the focal length,    -   a local variable characteristic, which is variable within a        given image 103, an example being coordinates x, y or rho, theta        in the image, permitting image-processing means P1 to apply        local processing that differs depending on the zone of the        image.

A measurable factor which is variable from one appliance P25 to anotherbut which is fixed from one image 103 to another that has been captured,modified or restituted by the same appliance P25 is not generallyconsidered to be a variable characteristic P6. An example is the focallength for an appliance P25 with fixed focal length.

The adjustments used as described hereinabove are examples of variablecharacteristics P6.

The formatted information 15 may depend on at least one variablecharacteristic P6.

By variable characteristic P6 there can be understood in particular:

-   -   the focal length of the optical system,    -   the redimensioning applied to the image (digital zoom factor:        enlargement of part of the image; and/or under-sampling:        reduction of the number of pixels of the image),    -   the nonlinear brightness correction, such as the gamma        correction,    -   the enhancement of contour, such as the level of deblurring        applied by appliance P25,    -   the noise of the sensor and of the electronic unit,    -   the aperture of the optical system,    -   the focusing distance,    -   the number of the frame on a film,    -   the underexposure or overexposure,    -   the sensitivity of the film or sensor,    -   the type of paper used in a printer,    -   the position of the center of the sensor in the image,    -   the rotation of the image relative to the sensor,    -   the position of a projector relative to the screen,    -   the white balance used,    -   the activation of a flash and/or its power,    -   the exposure time,    -   the sensor gain,    -   the compression,    -   the contrast,    -   another adjustment applied by the user of appliance P25, such as        a mode of operation,    -   another automatic adjustment of appliance P25,    -   another measurement performed by appliance P25.

In the case of a restitution means 19, the variable characteristic P6can also be defined as variable restitution characteristic.

Variable Characteristic Value

On the basis of FIG. 13, a description will now be given of the conceptof variable characteristic value P26. A variable characteristic valueP26 is defined as the value of variable characteristic P6 at the momentof capture, modification or restitution of a specified image, such valuebeing obtained, for example, from data in Exif format present in fileP100. Image-processing means P1 can then process or modify the qualityof image 103 as a function of variable characteristics P6, by usingformatted information 15 that depends on variable characteristics P6 andby determining the value P26 of the variable characteristics.

In the case of a restitution means 19, the value of variablecharacteristic P6 can also be defined as a variable restitutioncharacteristic.

Measured Formatted Information, Extended Formatted Information

As illustrated in FIG. 15, the formatted information 15 or a fraction ofthe formatted information 15 can include measured formatted informationP101 to illustrate a raw measurement, such as a mathematical fieldrelated to geometric distortion defects at a certain number ofcharacteristic points of an array 80. As illustrated in FIG. 15, theformatted information 15 or a fraction of the formatted information 15can include extended formatted information P102, which can be calculatedfrom measured formatted information P101, for example by interpolationfor real points other than the characteristic points of array 80. In theforegoing, it has been seen that a formatted information item 15 mightdepend on variable characteristics P6. According to the invention, acombination P120 is defined as a combination composed of variablecharacteristics P6 and of values P26 of variable characteristics, anexample being a combination P120 composed of the focal length, of thefocusing, of the diaphragm aperture, of the capture speed, of theaperture, etc. and of associated values. It is difficult to imagine howthe formatted information 15 related to different combinations P120 canbe calculated, all the more so because certain characteristics ofcombination P120, such as the focal length and the distance, can varycontinuously.

The invention provides for calculating the formatted information 15 inthe form of extended formatted information P102 by interpolation frommeasured formatted information P101 related to a predetermined selectionof combinations P120 of known variable characteristics P6.

For example, measured formatted information P101 related to thecombination P120 of “focal length=2, distance=7, capture speed= 1/100”,to the combination of “focal length=10, distance=7, capture speed=1/100” and to the combination of “focal length=50, distance=7, capturespeed= 1/100” is used to calculate extended formatted information P102that depends on focal length as the variable characteristic P6. By meansof this extended formatted information P102, it is possible inparticular to determine formatted information related to the combinationof “focal length=25, distance=7 and capture speed= 1/100”.

The measured formatted information P101 and the extended formattedinformation P102 may exhibit an interpolation deviation P121. Theinvention may include the stage of selecting zero or one or morevariable characteristics P6, such that interpolation deviation P121 forthe extended formatted information P102 obtained for the variablecharacteristics P6 selected in this way is smaller than a predeterminedinterpolation threshold. In fact, certain variable characteristics P6may have a smaller influence than others on the defect P5, and the errorintroduced by making the approximation that these are constant maymerely be minimum; for example, the focusing adjustment may have merelya slight influence on the vignetting defect, and for this reason may notbe part of the variable characteristics P6 selected. The variablecharacteristics P6 may be selected at the moment of production of theformatted information 15. It results from the combination of technicalfeatures that the modification of image quality employs simplecalculations. It also results from the combination of technical featuresthat the extended formatted information P102 is compact. It also resultsfrom the combination of technical features that the eliminated variablecharacteristics P6 have the least influence on the defect P5. It resultsfrom the combination of technical features that image quality can bemodified with specified precision by means of the formatted information15.

In the case of a restitution means 19, the combination 120 may also bedefined as a restitution combination.

In the case of a restitution means 19, the measured formattedinformation P101 may also be defined as measured formatted restitutioninformation.

In the case of a restitution means 19, the extended formattedinformation P102 may also be defined as extended formatted restitutioninformation.

In the case of a restitution means 19, the interpolation deviations P121may also be defined as interpolation restitution deviations.

Parameterizable Model, Parameters

Referring in particular to FIGS. 5, 6 and 16, a description will now begiven of the concept of parameters P9 and parameterizable model P10.Within the meaning of the invention, a parameterizable model P10 isdefined as a mathematical model that may depend on variables P6 and thatmay be related to one or more defects P5 of one or more appliances P25;parameterizable transformation model 12, parameterizable reversetransformation model 212, parameterizable restitution transformationmodel 97 and parameterizable restitution transformation model 297described hereinabove are examples of parameterizable models P10; forexample, a parameterizable model P10 may be related to:

-   -   sharpness defects or blurring of a digital photo appliance,    -   geometric vignetting defects of a photo appliance which it is        hoped can be emulated,    -   geometric distortion defects and geometric chromatic aberration        defects of a projector,    -   sharpness or blurring defects of a disposable photo appliance        combined with a scanner.

The formatted information 15 related to a defect P5 of an appliance P25may be presented in the form of the parameters P9 of a parameterizablemodel P10 depending on variable characteristics P6; by means of theparameters P9 of parameterizable model P10, it is possible to identify amathematical function P16 in a set of mathematical functions, such asmulti-variable polynomials; by means of the mathematical functions P16,it is possible to modify image quality as a function of specified valuesof the variables P6.

In such a way that image-processing means P1 can use the parameters P9of parameterizable transformation model P10 to calculate the modifiedimage, for example to calculate the corrected intensity or the correctedrestitution intensity of a point of the image.

Color Plane

Referring in particular to FIG. 15, a description will now be given ofthe concept of color plane P20 of a colored image 103. Image 103 can bedecomposed into color planes P20 in various ways: number of planes (1, 3or more), precision (8 bits unsigned, 16 bits signed, floating, etc.)and significance of the planes (relative to a standard color space).Image 103 can then be decomposed in various ways into color planes P20:red color plane composed of red pixels, green color plane, blue colorplane (RGB) or brightness, saturation, hue, etc.; on the other hand,color spaces such as PIM exist, or negative pixel values are possible inorder to permit representation of subtractive colors, which cannot berepresented in positive RGB; finally, it is possible to encode a pixelvalue on 8 bits or 16 bits, or by using floating values. As an exampleof how the formatted information 15 may be related to the color planesP20, the sharpness defects can be characterized differently for theplanes of red, green and blue color, to permit image-processing means P1to correct the sharpness defect differently for each color plane P20.

Provision of the Formatted Information

On the basis in particular of FIGS. 8, 13, 15 and 16, a description willnow be given of an alternative embodiment of the invention. To provideformatted information 15 in a standard format to image-processing meansP1, the system includes data-processing means and the method includesthe stage of filling in at least one field 91 of the standard formatwith the formatted information 15. Field 91 may then contain inparticular:

-   -   values related to the defects P5, for example in the form of        parameters P9, in such a way that image-processing means P1 can        use the parameters P9 to modify image quality by taking the        defects P5 into account, and/or    -   values related to the sharpness defects, for example in the form        of parameters P9, in such a way that image-processing means P1        can use the parameters P9 to modify image quality by taking the        sharpness defects into account, and to calculate the corrected        shape or the corrected restitution shape of a point of the        image, and/or    -   values related to the colorimetry defects, for example in the        form of parameters P9, in such a way that image-processing means        P1 can use the parameters P9 to modify image quality by taking        the colorimetry defects into account, and to calculate the        corrected color or the corrected restitution color of a point of        the image, and/or    -   values related to the geometric distortion defects and/or to the        geometric chromatic aberration defects, for example in the form        of parameters P9, in such a way that image-processing means P1        can use the parameters P9 to modify image quality by taking the        geometric distortion defects and/or the geometric chromatic        aberration defects into account, and to calculate the corrected        position or the corrected restitution position of a point of the        image, and/or    -   values related to the geometric vignetting defects, for example        in the form of parameters P9, in such a way that        image-processing means P1 can use the parameters P9 to modify        image quality by taking the geometric vignetting defects into        account, and to calculate the corrected intensity or the        corrected restitution intensity of a point of the image, and/or    -   values related to the deviations 14, and/or    -   values that are functions of variable characteristics P6        depending on image 103, for example the polynomial coefficients        and terms which depend on the variable characteristic P6        corresponding to the focal length and with which it is possible        to calculate the corrected intensity of a point of the image as        a function of its distance from the center, in such a way that        the image-processing means can calculate the corrected intensity        of a point for any value of focal length of the image-capture        appliance at the moment at which image 103 was captured,    -   values related to formatted information related to the color        planes P20,    -   values related to formatted information,    -   values related to measured formatted information,    -   values related to extended formatted information.

Production of Formatted Information

On the basis in particular of FIGS. 7, 12 and 17, a description will nowbe given of an alternative embodiment of the invention. To produceformatted information 15 related to the defects P5 of the appliances P25of an appliance chain P3, the invention can employ data-processing meansand the first algorithm and/or second algorithm and/or third algorithmand/or fourth algorithm and/or fifth algorithm and/or sixth algorithmand/or seventh algorithm and/or eighth algorithm as describedhereinabove.

Application of the Invention to Cost Reduction

Cost reduction is defined as a method and system for lowering the costof an appliance P25 or of an appliance chain P3, especially the cost ofthe optical system of an appliance or of an appliance chain, the methodconsisting in:

-   -   reducing the number of lenses, and/or    -   simplifying the shape of the lenses, and/or    -   designing an optical system having defects PS that are larger        than those desired for the appliance or the appliance chain, or        choosing the same from a catalog, and/or    -   using materials, components, processing operations or        manufacturing methods that are less costly for the appliance or        the appliance chain and that add defects P5.

The method and system according to the invention can be used to lowerthe cost of an appliance or of an appliance chain: it is possible todesign a digital optical system, to produce formatted information 15related to the defects P5 of the appliance or of the appliance chain, touse this formatted information to enable image-processing means P1,whether they are integrated or not, to modify the quality of imagesderived from or addressed to the appliance or to the appliance chain, insuch a way that the combination of the appliance or the appliance chainwith the image-processing means is capable of capturing, modifying orrestituting images of the desired quality at reduced cost.

1. A method for providing formatted information in a standard format toan image-processor, the formatted information being related to defectsof an appliance chain, the appliance chain including at least oneimage-capture appliance and/or one image-restitution appliance, theimage-processor using the formatted information to modify quality of atleast one image derived from or addressed to the appliance chain; theformatted information including: data characterizing the defects of theimage-capture appliance; and/or data characterizing the defects of theimage-restitution appliance, said defects of the image-capture applianceor of the image-restitution appliance being related to characteristicsof an optical system or of a sensor or of an electronic unit or ofsoftware integrated in the at least one image-capture appliance; andsaid defects including at least one of geometric defects, sharpnessdefects, colorimetry defects, geometric distortion defects, geometricchromatic aberration defects, geometric vignetting defects, contrastdefects, colorimetry defects, rendering of colors and color cast,defects of flash uniformity, sensor noise, grain, astigmatism defects,and spherical aberration defects; the method comprising: storing theformatted information in a database of characteristics integrated into aremote server; filling in, by a data-processing unit integrated in theremote server, at least one field of the standard format with theformatted information, the field being designated by a field name, thefield containing at least one field value; providing, from the remoteserver, the formatted information in the standard format to theimage-processor; the formatted information being composed at leastpartly of parameters of a parameterizable transformation modelrepresentative of defects of the image-capture appliance and/or of theimage-restitution appliance; and the method comprising a calculationalgorithm for choosing between a set of parameterizable transformationmodels, one minimizing a difference between a first image and a secondimage, wherein the first image is an image obtained by application ofthe parameterizable transformation model to an image of a referencescene obtained with the image-capture appliance or image-restitutionappliance, and the second image is an image obtained by mathematicalprojection of the reference scene.
 2. A method according to claim 1,wherein the field is related to sharpness defects of the image-captureappliance and/or of the image-restitution appliance, wherein the fieldcontains at least one value related to the sharpness defects of theimage-capture appliance and/or of the image-restitution appliance.
 3. Amethod according to claim 1, wherein the field is related to colorimetrydefects of the image-capture appliance and/or of the image-restitutionappliance, wherein the field contains at least one value related to thecolorimetry defects of the image-capture appliance and/or of theimage-restitution appliance.
 4. A method according to claim 1, whereinthe field is related to geometric distortion defects and/or to geometricchromatic aberration defects of the image-capture appliance and/or ofthe image-restitution appliance, wherein the field contains at least onevalue related to the geometric distortion defects and/or to thegeometric chromatic aberration defects of the image-capture applianceand/or of the image-restitution appliance.
 5. A method according toclaim 1, wherein the field is related to geometric vignetting defectsand/or to contrast defects of the image-capture appliance and/or of theimage-restitution appliance, wherein the field contains at least onevalue related to the geometric vignetting defects and/or to the contrastdefects of the image-capture appliance and/or of the image-restitutionappliance.
 6. A method according to claim 1, wherein the field containsat least one value related to deviations.
 7. A method according to claim2, wherein the formatted information is composed at least partly ofparameters of a parameterizable transformation model representative ofthe sharpness defects of the image-capture appliance and/or of theimage-restitution appliance, wherein the value or values contained inthe field related to the sharpness defects are composed at least partlyof parameters of the parameterizable transformation model; wherein theimage-processor can use the parameters of the parameterizabletransformation model to calculate a corrected shape or correctedrestitution shape of a point of the image.
 8. A method according toclaim 3, wherein the formatted information is composed at least partlyof parameters of a parameterizable transformation model representativeof the colorimetry defects of the image-capture appliance and/or of theimage-restitution appliance, wherein the value or values contained inthe field related to the colorimetry defects are composed at leastpartly of parameters of the parameterizable transformation model;wherein the image-processor can use the parameters of theparameterizable transformation model to calculate a corrected color orcorrected restitution color of a point of the image.
 9. A methodaccording to claim 4, wherein the formatted information is composed atleast partly of parameters of a parameterizable transformation modelrepresentative of the geometric distortion defects and/or of thegeometric chromatic aberration defects of the image-capture applianceand/or of the image-restitution appliance, wherein the value or valuescontained in the field related to the geometric distortion defectsand/or to the geometric chromatic aberration defects are composed atleast partly of parameters of the parameterizable transformation model;wherein the image-processor can use the parameters of theparameterizable transformation model to calculate a corrected positionor the corrected restitution position of a point of the image.
 10. Amethod according to claim 5, wherein the formatted information iscomposed at least partly of parameters of a parameterizabletransformation model representative of the geometric vignetting defectsand/or of the contrast defects of the image-capture appliance and/or ofthe image-restitution appliance, wherein the value or values containedin the field related to the geometric vignetting defects and/or to thecontrast defects are composed at least partly of parameters of theparameterizable transformation model; wherein the image-processor canuse the parameters of the parameterizable transformation model tocalculate a corrected intensity or the corrected restitution intensityof a point of the image.
 11. A method according to claim 1, wherein toprovide the formatted information in a standard format to theimage-processor, the method further comprises associating the formattedinformation with the image.
 12. A method according to claim 11, whereinthe image is transmitted in a form of a file, the file furthercontaining the formatted information.
 13. A method according to claim 1,wherein the image-capture appliance and/or the image-restitutionappliance includes at least one variable characteristic depending on theimage, at least one of the defects of the image-capture appliance and/orof the image-restitution appliance depending on the at least onevariable characteristic, wherein at least one of the fields contains atleast one value that is a function of the at least one variablecharacteristic depending on the image; wherein the image-processor canprocess the image as a function of the variable characteristics.
 14. Amethod according to claim 1, wherein the formatted information ismeasured formatted information, at least in part.
 15. A method accordingto claim 1, wherein the formatted information is extended formattedinformation, at least in part.
 16. A method according to claim 1,wherein the image is composed of color planes, the formatted informationbeing at least partly related to the color planes.
 17. A system forproviding formatted information in a standard format to animage-processor, the formatted information being related to defects ofan appliance chain, the appliance chain including at least oneimage-capture appliance and/or one image-restitution appliance, theimage-processor using the formatted information to modify quality of atleast one image derived from or addressed to the appliance chain; theformatted information including: data characterizing the defects of theimage-capture appliance; and/or data characterizing the defects of theimage-restitution appliance, said defects of the image-capture applianceor of the image-restitution appliance being related to characteristicsof an optical system or of a sensor or of an electronic unit or ofsoftware integrated in the at least one image-capture appliance; andsaid defects including at least one of geometric defects, sharpnessdefects, colorimetry defects, geometric distortion defects, geometricchromatic aberration defects, geometric vignetting defects, contrastdefects, colorimetry defects, rendering of colors and color cast,defects of flash uniformity, sensor noise, grain, astigmatism defects,and spherical aberration defects; the system comprising: a database ofcharacteristics integrated into a remote server, the database ofcharacteristics configured to store the formatted information; and adata-processor integrated in the remote server, the data-processorconfigured to fill in at least one field of the standard format with theformatted information, the field being designated by a field name, thefield containing at least one field value, wherein the formattedinformation in the standard format is provided from the remote server tothe image-processor, and the formatted information is composed at leastpartly of parameters of a parameterizable transformation modelrepresentative of defects of the image-capture appliance and/or of theimage-restitution appliance, and a calculation algorithm is used forchoosing between a set of parameterizable transformation models, oneminimizing a difference between a first image and a second image,wherein the first image is an image obtained by application of theparameterizable transformation model to an image of a reference sceneobtained with the image-capture appliance or image-restitutionappliance, and the second image is an image obtained by mathematicalprojection of the reference scene.
 18. A system according to claim 17,wherein the field is related to sharpness defects of the image-captureappliance and/or of the image-restitution appliance, wherein the fieldcontains at least one value related to the sharpness defects of theimage-capture appliance and/or of the image-restitution appliance.
 19. Asystem according to claim 17, wherein the field is related tocolorimetry defects of the image-capture appliance and/or of theimage-restitution appliance, wherein the field contains at least onevalue related to the colorimetry defects of the image-capture applianceand/or of the image-restitution appliance.
 20. A system according toclaim 17, wherein the field is related to geometric distortion defectsand/or to geometric chromatic aberration defects of the image-captureappliance and/or of the image-restitution appliance, wherein the fieldcontains at least one value related to the geometric distortion defectsand/or to the geometric chromatic aberration defects of theimage-capture appliance and/or of the image-restitution appliance.
 21. Asystem according to claim 17, wherein the field is related to geometricvignetting defects and/or to contrast defects of the image-captureappliance and/or of the image-restitution appliance, wherein the fieldcontains at least one value related to the geometric vignetting defectsand/or to the contrast defects of the image-capture appliance and/or ofthe image-restitution appliance.
 22. A system according to claim 17,wherein the field contains at least one value related to deviations. 23.A system according to claim 18, wherein the formatted information iscomposed at least partly of parameters of a parameterizabletransformation model representative of the sharpness defects of theimage-capture appliance and/or of the image-restitution appliance,wherein the value or values contained in the field related to thesharpness defects are composed at least partly of parameters of theparameterizable transformation model.
 24. A system according to claim19, wherein the formatted information is composed at least partly ofparameters of a parameterizable transformation model representative ofthe colorimetry defects of the image-capture appliance and/or of theimage-restitution appliance, wherein the value or values contained inthe field related to the colorimetry defects are composed at leastpartly of parameters of the parameterizable transformation model.
 25. Asystem according to claim 20, wherein the formatted information iscomposed at least partly of parameters of a parameterizabletransformation model representative of the geometric distortion defectsand/or of the geometric chromatic aberration defects of theimage-capture appliance and/or of the image-restitution appliance,wherein the value or values contained in the field related to thegeometric distortion defects and/or to the geometric chromaticaberration defects are composed at least partly of parameters of theparameterizable transformation model.
 26. A system according to claim21, wherein the formatted information is composed at least partly ofparameters of a parameterizable transformation model representative ofthe geometric vignetting defects and/or of the contrast defects of theimage-capture appliance and/or of the image-restitution appliance,wherein the value or values contained in the field related to thegeometric vignetting defects and/or to the contrast defects are composedat least partly of parameters of the parameterizable transformationmodel.
 27. A system according to claim 17, wherein to provide theformatted information in a standard format to the image-processor, thesystem further comprises a second data-processor configured to associatethe formatted information with the image.
 28. A system according toclaim 27, further comprising a transmission device to transmit the imagein a form of a file, the file further containing the formattedinformation.
 29. A system according to claim 17, wherein theimage-capture appliance and/or the image-restitution appliance includeat least one variable characteristic depending on the image, at leastone of the defects of the image-capture appliance and/or of theimage-restitution appliance depending on the at least one variablecharacteristic, wherein at least one of the fields contains at least onevalue that is a function of the at least one variable characteristicdepending on the image.
 30. A system according to claim 17, wherein theformatted information is measured formatted information, at least inpart.
 31. A system according to claim 17, wherein the formattedinformation is extended formatted information, at least in part.
 32. Asystem according to claim 17, wherein the image is composed of colorplanes, the formatted information being at least partly related to thecolor planes.
 33. A method according to claim 1, wherein representeddefects are part of defects related to sharpness, colorimetry, geometricdistortion, geometric chromatic aberration, geometric vignetting and/orof contrast defects.